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Teknillinen korkeakoulu Department of Industrial Engineering and Management Laboratory of Industrial Management
OPERATIONS MANAGEMENT AS A PROBLEM-SOLVING DISCIPLINE: A DESIGN SCIENCE APPROACH Jan Holmström, Teknillinen korkeakoulu Ari-Pekka Hameri, University of Lausanne Mikko Ketokivi, Teknillinen korkeakoulu Working Paper No 2006/1 Espoo, Finland 2006
Teknillinen korkeakoulu Department of Industrial Engineering and Management Laboratory of Industrial Management Teknillinen korkeakoulu Department of Industrial Engineering and Management Laboratory of Industrial Management Teknillinen korkeakoulu Department of Industrial Engineering and Management Laboratory of Industrial Management PO Box 5500 FI-02015 TKK, Espoo, Finland Telephone int 358 9 451 2846 Fax int 358 9 451 3665 Internet http://www.tuta.tkk.fi/teta This working paper is also available on the web: http://www.tuta.hut.fi/library/working_paper/pdf/Holmstrom_et_at_2006.pdf
Operations Management as a Problem-Solving Discipline: A Design Science Approach
Jan Holmström
Academy Research Fellow (Academy of Finland) Helsinki University of
Technology POB 5500, FI-02015 HUT,
Finland
t: +358-50-367 5973 f: +358-9-451 3665
e-mail: [email protected]
Ari-Pekka Hameri Professor of Operations
Management HEC-Lausanne, University of
Lausanne BFSH-1, CH-1015 Lausanne,
Switzerland
t: +41-21-692 3460 f: +41-21-692 3495
e-mail: [email protected]
Mikko Ketokivi Senior Lecturer
Helsinki University of Technology
POB 5500, FI-02015 HUT, Finland
t: +358-50-376 1095 f: +358-9-451 3665
e-mail: [email protected]
January 17, 2006
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Operations Management as a Problem-Solving Discipline: A Design Science Approach
Abstract
Some operations management researchers tackle real-life problems that are not well-defined, and
where the focus is on discovery and problem solving, not explanation. In this paper, we suggest
that the principles of design science can be used to formalize research on ill-structured operations
management problems, such as research addressing the impact of emergent technologies on the
performance frontier. Building on the work of Herbert Simon, we propose that a design science
approach can be used to complement the conventional approaches in operations management,
which are based on the methodologies adopted from the natural sciences, principally, Carl
Hempel’s hypothetico-deductive reasoning. The goal of this paper is to explicate an
epistemology and methodology for the logic of discovery and problem-solving in operations
management research. The process is illustrated through an in-depth case of a multi-year
research project concerning new information technologies, where the performance frontier was
affected through the development of applications that enabled the use of simpler integrating
mechanisms and thus performance improvements in multiple dimensions. The case example
illustrates why successful solution designs do not automatically translate to performance
improvements but require step-wise development and introduction of the solutions in practice. If
this is to be done under the rubric of scientific research, the proper methodology must be
explicated.
Keywords: Case study and field research, empirical research, theory development
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1. Introduction
“A major goal of empirical work in science is to discover new phenomena and
generate hypotheses for describing and explaining them and not simply to test
hypotheses that have already been generated. Indeed, theories cannot be tested
until they have been created, and creation takes place in the context of discovery,
not verification.” Klahr and Simon (1999, p. 529)
The standard that empirical research in general and experimental manipulation in particular
should seek to test hypotheses is the dominant position in contemporary literature on
methodology. This approach follows the legacy of logical empiricism (Hempel, 1965; Popper,
1963), and has been adopted in the organization sciences (Camerer, 1985) as well as operations
management (OM) (Schmenner and Swink, 1998). Good ideas must, however, start somewhere.
While theory-testing and explanation in general have been and remain indispensable, we will
argue in this paper—building primarily on Simon (1973)—that discovery and problem-solving
must be considered equally important to the advancement of OM as a science. Our position is
based on the observation that much of the important empirical work in science is indeed
conducted under the rubric of discovery, not theory testing. We further contend that discovery is
not simply a matter of “intuition” or “creativity”, it can have a logic and it can be subjected to
scientific inquiry (Simon, 1973).
In the OM literature, Jayanthi and Sinha (1998) have convincingly demonstrated that
even chaotic and ambiguous processes such as innovation can indeed be modeled rigorously.
They also aptly demonstrate that scientific knowledge can be created without specifying a priori
hypotheses. This is an important observation, but clearly such study may require, at least in part,
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reasoning and research of a different type than what we are used to. If we want to study, for
instance, high-technology operations, which are “prototypical of future operations” (Beckman
and Sinha, 2005), what could a justifiable philosophy of science be? We submit that in order to
answer this question, we have to examine OM through a design science lens.
OM is sometimes dubbed a “problem-solving discipline”, and some researchers have not
only examined problem-solving as a research topic, but indeed assumed themselves the role of a
problem-solver. The goal of this paper is to establish an epistemological and methodological
basis for OM as a problem-solving discipline, where the researcher assumes an active role in
problem-solving. Our approach parallels Eisenhardt’s (1989) idea of inductive case study,
although the focus in this paper is on actual problem-solving and discovery, not development of
explanatory theory of problem-solving. Eisenhardt’s approach, and related theory-building in
approaches general, do not address problem-solving and discovery—constructing novel theories
about poverty in the third world, for instance, is fundamentally distinct from “putting one’s
hands in the clay” and doing something about it. Our position is that both should be accepted as
part of scientific research so long as research is done in a rigorous manner.
To clarify, the aim of this paper is not to resurrect the age-old debate of theory building
vs. theory-testing. There is clearly a time and a place for both (Eisenhardt, 1989; Meredith,
1998), and we do not see any reason to revisit this debate. Rather, our position is that there
should also be a time and a place in OM research for discovery. Toward this end, we investigate
how OM researchers could take a systematic approach to solving real-life problems and
engaging in discovery, and doing all this under the rubric of scientific research. Of course,
problem-solving and discovery are complementary with theorizing in that many useful
theories—although by no means all of them—arise from attempts to solve real-life problems:
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Where did, for instance, the classic concepts and theories of the focused factory or tradeoffs
originate? Indeed, much of the early management research centered around addressing
managerial problems (Camerer, 1985).
1.1 Why is Problem-Solving and Discovery Relevant to OM?
There is a considerable bias in the extant OM literature toward problems and research questions
that are well-defined in their structure. We also often encourage our doctoral students to avoid
taking risks by sticking to well-defined research questions in their theses. While this is
understandable, we cannot avoid the issue that there are questions of both managerial and
theoretical import that can be relatively ill-structured, to use Simon’s (1973) terminology.
Further, we know that there is a scientific method to address ill-structured problems as well,
however, the reasoning is different in that the hypothetico-deductive research design is replaced
by abductive reasoning (e.g., Hartshorne and Weiss, 1934; Nesher, 2001).
To establish the relevance to OM, let us look at one of the prominent theories in OM.
According to the Theory of Performance Frontiers (Schmenner and Swink, 1998, p. 107), the
performance of a production system can be thought of as a position in an N-dimensional
performance space, and limits to the performance are defined by an N-dimensional canvas, the
performance frontier. This is fundamentally an applied micro-economic argument, building on
the concept of the production possibilities set (e.g., Varian, 2003, p. 584). In economic theory,
the ultimate performance frontier is defined by available technology, although other “policy
frontiers” may limit the de facto performance before the limits of the technology are reached
(Schmenner and Swink, 1998, p. 109). In addition to finding ways to move as close to the
performance frontier as possible, a highly relevant question for both OM theory and practice is:
How can the frontier be pushed even further for competitive benefit (e.g., Hayes et al., 2005, pp.
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64, 336)? This, we submit, is likely to be an ill-structured problem in that it likely involves
extensive problem-solving, development of new technologies and discovery.
It is highly likely that the performance frontier was redefined, for instance, in the advent
of emergent technologies such as the Internet, radio-frequency identification (RFID) chips, and
peer-to-peer computing. Finding new ways of managing operations may enable simultaneous
efficiency and service improvements, and is thus an important factor in changing the competitive
landscape. A case in point, Dell’s application of call-center and Internet technology to sell mass-
customized personal computers likely moved the performance frontier in the computer industry,
or at least for Dell—it is often useful to think of the efficiency frontier as firm-specific.
Specifically, being able to take advantage of the build-to-order strategy led to a simultaneous
improvement of service (customization) and efficiency (elimination of obsolescence). Because
Dell’s rivals could not match Dell’s achievements, it is reasonable to conclude that the
performance frontier was indeed shifted in Dell’s favor.
1.2 A Design Science Approach to OM
Conducting explorative research on the use of emergent technology in addressing OM problems
involves many methodological challenges. To meet the challenges associated with practical and
technological problem-solving research a design science approach has been proposed (e.g.,
Dasgupta, 2003; Niiniluoto, 1993; Simon, 1996). The design science or problem-solving
approach is fundamentally different from both the theory-building and theory-testing approaches,
which model themselves after the natural sciences and seek explanation. This was indeed
recognized by Hempel (1965) as well, who readily acknowledged that the types of reasoning that
he proposed—the deductive-nomological, deductive-statistical and inductive-statistical—were of
little use in trying to solve problems, even mundane ones such as trying to fix a broken radio
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(Hempel, 1965, p. 413). Hanson (1958, p. 1073), in turn, doubted “whether [the hypothetico-
deductive] method has anything to do with real discovery”.
In this paper, we adopt Simon’s (1973) definition of design science as research that is
focused on identifying and solving new problems and on understanding this problem-solving
process with the objective of improving the effectiveness of both problem discovery and problem
solving. We submit that OM, if viewed as a design science, should seek to introduce structure
and logic into problem discovery and problem solving, at the same time maintaining rigor in
evaluation and hypothesis testing. We offer the design science as a complement, not substitute,
to the conventional approaches (Klahr and Simon, 1999): discovery research cannot and must not
replace evaluation research, or vice versa.
The primary challenge in developing a design science methodology lies in the modeling,
quantification and validation of something that at best exists only as an idea or a design, or
something that has only been implemented in a few instances. Obviously, the theory-testing
approach is not appropriate in that too stringent requirements on representation, quantification
and validation in the early phases of problem solving may inhibit the exploration of many good
alternatives (Simon, 1996, p. 28). Fundamentally, there is nothing to be explained, hence,
explanatory theories clearly seem out of place.
We know that human problem-solvers cannot examine every proposed solution and
conjecture: “effective problem solving depends in large part on processes that constrain search
judiciously to the exploration of a few branches” (Klahr and Simon, 1999, p. 532). In this
situation, developing various heuristics to assess the novelty, relevance and functionality of a
proposed solution is an exploratory and satisficing way to proceed. This is the first way of
tackling the design science challenge. The challenge can further be addressed by seeking theory
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to describe how the research is proceeding and how the problem discovery and solving has been
performed in the past.
The idea that successful scientists are (and should be) as much active problem-solvers
and designers as they are observers and theorists is not new (Pickering, 1995; Simon, 1973), and
is at least indirectly supported by the editorial policies of the most prominent academic OM
journals, where “significant impact to practice” is often encouraged. We do not know how
significant impact to practice could be achieved if we excluded ill-structured problems from the
domain of OM research activity or only assumed the role of an evaluator of someone else’s
actions. In his statement of editorial policy as a new editor-in-chief, Handfield (Meredith et al.,
2002) discussed the generation of research ideas and their selection for further work. This
description can be interpreted as an attempt to highlight problem discovery and resolution in OM
research (see also Stuart et al., 2002). The assessment and selection of ideas is based on how
interesting, surprising, believable, and useful the ideas appear, and on “if-then” analysis of
articulated conjectures. Economics Nobel laureate Herbert Simon strongly and explicitly
disagreed with Popper’s opinion that there is no logic to scientific discovery (e.g., Simon, 1973).
Simon’s own epistemology built not only on his own work, but also the work of philosopher
Norwood Hanson (e.g., Hanson, 1958), and at least implicitly on philosopher Charles Peirce’s
work on abductive reasoning (e.g., Nesher, 2001).
There are very few OM studies that adopt a design science perspective. Perhaps the
closest to a design science approach in the extant literature is Hayes et al. (2005, p. 209), who
present a framework for operations development, problem solving and experimentation. At the
same time, the framework does not attempt to describe in detail how new ways of operating are
discovered, nor how these new ways to operate could be incorporated into a growing body of
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OM theory. The framework is simply a rudimentary device for operations managers for deciding
whether learning by doing or learning by modeling is more appropriate. The framework is
however important as it highlights the need for a sustained capability for trial and error, which
common organizational practices such as imitating successful competitors instead of developing
own new alternatives tend to undermine (March, 1991).
Better guidance on how to search for new alternatives and how to develop these further
would be useful especially in a rapidly changing technological environment (Beckman and
Sinha, 2005). The remainder of this paper addresses problem discovery, problem solving and
solution implementation in OM. We will first juxtapose the design science epistemology and
methodology with the current mainstream OM approaches and describe the design science
methodology in detail. The design science approach for exploring the performance frontier is
then illustrated using Internet-based workflow management. Finally, the implications of the
study on OM as a problem-solving discipline are discussed.
2. How Does Design Science Fit the Extant Philosophy of Science in OM?
The vast majority of OM research models itself after logical empiricists such as Carl Hempel and
Karl Popper (e.g., Schmenner and Swink, 1998). The statistical reasoning often used in
conjunction with testing theories and scientific explanation in general, in turn, owes much to
philosophers Rudolph Carnap, Ernst Nagel and Wesley Salmon, although we must bear in mind
that the logical empiricists exhibited strong disagreement even on some of the fundamental
concepts, the proper criteria for inductive reasoning being a case in point (Salmon, 1983, pp.
557-558). This aside, that OM research is positivist is openly acknowledged (Meredith et al.,
2002, p. 11). This is however problematic, because it is well known in mainstream philosophy
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that “to declare oneself a positivist [today] is… to admit to holding a logically incoherent
position” (Moldoveanu and Baum, 2002). Positivism is dead, both in the natural and social
sciences (McKelvey, 1997). Indeed, many of its key tenets have been obsolete for decades
(Quine, 1951), and it was deemed philosophically untenable by the very people who developed
it. What then do we mean when we say we are positivists? More importantly, what is the de facto
manner in which we claim to create new knowledge, what is our “logic-in-use” (Kaplan, 1964)?
Is our philosophy of science tenable? These are important question to address before we can
position the design science epistemology vis-à-vis the OM mainstream.
Gorski (2004) claims that even those of us following the positivistic legacy cannot model
our work after Hempel’s hypothetico-deductive (HD) model: that the HD model does not work,
Gorski claims, is one of the “worst-kept secrets” in the social sciences. Gorski’s statement is not
one of research policy, because he criticizes the use of the HD method form within the paradigm,
on its own turf: he has nothing against the idea itself, but points out that social scientists do not
and cannot use the HD method the way it was intended—what is interesting is that Hempel
himself would agree (Hempel, 1965, p. 412). That the HD method does not work is a
discomforting thought, but cannot be side-stepped: How often do we in our theorizing invoke
general laws in our explanation, which is a fundamental requirement in Hempel’s model? How
many OM authors indeed logically deduce hypotheses from their theories (this is the very
stringent requirement of Hempel’s method, hence the name hypothetico-deductive)? Our theories
often invoke theoretical concepts such as “synergy”, “competence”, “uncertainty”, “rarity”,
“inimitability” and “performance”, the reduction of which into purely empirical statements (for
verification to be possible) is impossible (e.g., McKelvey, 1999, p. 403)? How many of us have
really tried to falsify a theory? Which theories in OM have been hitherto falsified?
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At the same time, we must keep in mind that Hempel never himself claimed that an
explanation must be a deductive one in order to be good (Gorski, 2004, p. 6) or that working
scientists actually formulate their explanations according to the HD method; Hempel never
wanted to impose his model on all kinds of reasoning as “a simple hypothetico-deductive
construal affords no adequate general explication of confirmation” (Hempel, 1983, p. 570). It is
also well known that using Hempel’s method is downright toothless if the researcher’s goal is to
present causal arguments (Salmon, 1971, p. 37). To be sure, OM research is abundant with
causal arguments.
It is now widely recognized that all alleged positivists in OM (and organization sciences
in general) are actually scientific realists of sorts (McKelvey, 1997; 1999; Moldoveanu and
Baum, 2002), and we use some variant of Salmon’s (1971) statistical relevance (S-R) model of
reasoning. In our daily work as “bench scientists”, we have indeed abandoned many of the tenets
of logical positivism and its successor logical empiricism, and for a good reason: these tenets are
simply untenable. For instance, we often use theoretical concepts that are not reducible to
observation statements (for a logical positivist this is unacceptable), and we use rules of
correspondence between theoretical and empirical concepts that the positivist would, to be sure,
consider downright unscientific (e.g., on what grounds is the CFO’s assessment of the return on
equity on a survey questionnaire a measure of the focal company’s financial performance?).
Some claim that they use operational definitions, but they are not operational definitions in the
philosophical meaning of the word (e.g., Bridgman, 1927), the vast majority of definitions and
the associated rules of correspondence are based on scientific realism (Bagozzi, 1980; Keat and
Urry, 1975). Of course, we often present causal claims in OM, although we acknowledge the
well-known fact that statistical association does not prove causality.
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The purpose of this discussion is not to propose that the philosophy of science of
mainstream OM is untenable, let alone try to reject it. But we do wish to propose that those who
claim that OM is a positivist science are wrong and that we should stop using the term altogether
to describe our epistemology and methodology: our use of terminology is confusing and indeed
highly inconsistent with mainstream philosophy. What is important from the point of view of this
paper is that the untenable tenets of positivism must not be used as criteria in assessing
methodologies and research strategies in OM. That propositions or solutions cannot be verified
or even falsified is not just a potential shortcoming of the design science approach, it is a
shortcoming in all OM research, including the theory-testing approaches. But we knew this
already decades ago: every scientific approach falls short of the positivist ideals (e.g., Quine,
1951). Of course, this does not mean that all the ideas that positivists presented are untenable.
The ultimate objective of the design science approach proposed in this paper is similar to
existing approaches in OM: design scientists seek objective (or at least intersubjective)
knowledge that can ultimately be expressed in a theoretical language, abstracted from the
immediate context in which it was developed. This was indeed Simon’ approach to design
science as well. In this sense the ontological and epistemological assumptions with extant OM
research are similar, and there are no incommensurability issues: design science and mainstream
OM research are based on a scientific realist ontology and objectivist epistemology—but not
positivism—to use Burrell and Morgan’s (1979) widely used and established terminology. The
only difference is that the design scientist tackles phenomena and problems that are not well-
defined, and where the solution cannot be reached by collecting data and assembling of evidence
through methods of deduction and induction: in the design science approach, evidence is not
collected, it is created as the discovery process unfolds, which is indeed typical of many kinds of
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process-oriented research (Poole et al., 2000). The design scientist also assumes a more active
role in shaping the phenomenon of interest, indeed taking the role of a problem-solver. It would
seem folly to prohibit the OM scientist from shaping the phenomenon so long as scientific
standards are followed in evaluating the interventions.
3. The Anatomy of a Design Science
In practice, design science encompasses research in engineering, medicine, management and
other areas of applied science which focuses on identifying problems, solving them and
understanding the problem-solving process (Niiniluoto, 1993). In order to support OM as a
design science discipline, the OM research community needs a description of the structure of
problem discovery and problem solving.
Simon (1996) advocated the use of the satisficing concept as the guide to the exploration
of evolving solution alternatives. The term itself originates in his earlier work on organization
theory with James March (March and Simon, 1958) and refers to identifying solutions that are
“good enough”. In the early stages of a design research process the aspiration is to design and
test novel, relevant and functional solution alternatives, with less concern about optimality. This
way satisficing can be seen as a procedure that describes how the new alternatives that are
implicit in the concept of a moving performance frontier can be discovered.
McKelvey (2002, p. 763) describes the epistemological relationship between theory,
model, and phenomena as a semantic conception, where theory, model and phenomena are
viewed as a chain of linked but autonomous agents. Combining this semantic conception with
satisficing in an environment characterized by technological change means that the relationship
between the semantic entities, or artifacts, needs to be described as an emergent relationship.
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The emergent relationship is necessary because we are addressing new phenomena and ill-
structured problems: in the beginning there may be just an idea, and in the end there are a
multitude of phenomena, models, and, sometimes explanatory theory. Figure 1 illustrates the
semantic conception and the emergent nature of design research. The idea of a new design or
solution leads through implementation of the design to the creation of new phenomena. These
phenomena are the result of the design and problem-solving activity and are thus dubbed
artificial phenomena by Simon (1996), “artificial” meaning “man-made, as opposed to natural”
(Simon, 1996, p. 4). In addition, it is important to bear in mind that these phenomena include
both the intended and unintended consequences of the design (Popper, 1963, p. 461). The
intended consequences are fulfilled design goals while the unintended often are new, emergent
problems that require further attention from the scientist.
Insert Figure 1 here
Theory development is separate from the design and phenomena in the emergent and
semantic conception of design science, although progress in both solution design and theoretical
explanation depends on what McKelvey (2002, p. 765) calls ontological adequacy: How well
does the model represent real-world phenomena? This is an ontological position, because the
criterion is one of representation, not prediction. The role of the theory is to explain both the
intended and unintended phenomena of the design, as well as the process of problem solving and
explanation itself. A design science theory of OM would be able to describe the structures of
problem discovery and problem solving. A design science theory could, for instance, describe the
design principles for creating complex systems that survive, independently of the technology that
is used (Weber, 1987, p. 8). This aim of design science theory can be compared to the aim of a
scientific paradigm (Kuhn, 1970) in that such principles would provide a coherent structure for
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problem solving, education, and research. Problem-solving research in OM does not currently
have such coherent structure.
The emergent and semantic conception of design science presented in figure 1 have to be
described in greater detail. We have done this in figure 2, which illustrates how a design
conjecture may progress to a stage when there are already numerous implementations, and the
structure of problem discovery and solution can be described.
Insert Figure 2 here
Figure 2 illustrates the emergent dimension of problem-solving and design research
explicitly. Base case is a description of the problem to be solved, including an outline of a
possible solution and the expected result. Solution designs are the specifications of solutions that
are detailed enough to be implemented in a test environment or in practice. Implementations are
the actual applications of the solution designs in practice. Evaluation refers to the analysis of
intended and unintended consequences of the implementations. Middle-range theories are the
refined base cases that take into account the results of the evaluations. Therefore, theory in this
context does not mean as much an explanation as it means solution to a problem.
Representations are descriptions of problems and solution designs that can be formally analyzed.
Theories of deep-structures (Weber, 1987) are theories explaining both intended and unintended
consequences of the designs.
The emergent dimension of design is trial and error, based on testing, prototyping and
evaluation. Implementation and testing are necessary to arrive at functional designs and
eliminate false assumptions (Simon, 1996). Implementation and testing have a pivotal role in the
design process as they provide feedback from practice and thus material for evaluation.
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Implementation and testing are needed for design science to be a self-correcting scientific realist
method (McKelvey, 2002).
The first steps in problem solving are the most problematic to describe rigorously (figure
2). How do we identify a base case for a new solution design and how do we describe it? Two
approaches have been proposed: abductive reasoning and use of heuristics. Abductive reasoning
(e.g., Hartshorne and Weiss, 1934), or more generally, the inference to the “best explanation”
(Harman, 1965), has indeed been proposed as a specific type of logic and reasoning used in
discovery (Nesher, 2001). Weick (1989), in turn, has proposed the use of “disciplined
imagination” and heuristics in developing theories. We submit that the idea of disciplined
imagination can also be extended to the identification the base case.
After the base case has been formulated the focus of research can shift to solution design
and implementation trials (figure 2). As the base case is validated, the solution is a design which
is implemented, and its effect can then be evaluated, after which the base case may become
middle range theory (Weick, 1989, p. 525). Evaluation is the critical activity in moving from
base case to middle-range theory. This is because there is a difference between intended design
and real-world implementation. As the evolutionary theorist Dennett (2003, p. 50) observes: it is
necessary that the “un-designed” features that are introduced when implementing in practice can
first be identified and later co-opted in the intended designs.
A fundamental epistemological challenge for developing the heuristics and describing a
procedure that facilitate problem discovery and solution designs lies in how conjectures and
tentative solutions are to be represented and documented. One possibility is to formulate design
propositions as a technical norm (Niiniluoto, 1993), the technological rule (Popper, 1963), or
means-ends propositions (Simon, 1996). The common structure of these representations is a
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statement describing what should or should not be done in a situation to achieve some specific
goal: “One wants to attain x. Unless y is done, x will not be attained. Therefore y must be done”.
Von Wright (1963, p. 161) labels this type of reasoning practical inference. This bears a striking
resemblance to Simon’s (Simon, 1973, p. 473) idea of a “purpose-oriented logic”, where he
suggests that it is logical (in a practical, not deductive sense) to do y if (a) one wishes to attain x,
and (b) x cannot be attained unless y is done. Practical inference thus provides the required
methodological basis for reasoning in design science.
A proposition presented in the form of such a technical norm is not a descriptive
statement of the world. Rather, it is a conjecture on what should (or should not) be done in a
specific situation in order to achieve the desired goals. As such, it is different from what we
normally view as a theoretical proposition in management research (e.g., Whetten, 1989).
Further, this proposition is what the base case in figure 2 represents. Whether this conjecture is
right or wrong will be determined by testing. If the base case is a structured representation of the
design intention, it should be testable in an inter-subjective (Popper, 1959, p. 44) manner, using
tools such as conceptual analysis, modeling, and experimentation (Niiniluoto, 1993).
Presenting solution designs explicitly in terms of goals, situation analysis, and proposed
actions supports the further development, testing and replication of a design construct under
different circumstances and with increasing availability of empirical data. The three-part
definition of the technical norm is systematically used in defining solution patterns in
architecture and software systems design. The advantage has been significant in terms of
documenting, communicating, and collaboratively improving effective solutions (Alexander,
1977; Gamma et al., 1995). This way the technical norm links three types of decisions—know-
how, know-when, and know-why (Mahoney and Sanchez, 2004, p. 41)—needed in developing
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new middle-range management theory. In Figure 2 the base case of step ‘1’ becomes middle-
range theory in steps ‘2’ and ‘3’.
In the last step (step ‘3’ in figure 2), the problem discovery, the problem solving, and the
solutions are described and structured theoretically. Understanding the deeper structure of
problem discovery and the problem-solving process is important both for the researcher and the
practitioner. Finding a solution to a novel OM problem may depend on representing the problem
differently, and on realizing that it is the problem representation that needs to be changed.
A stop rule or design goal is critical for satisficing to be possible (Simon, 1996). Such
rules are needed at each level of the problem solving activity outlined in figure 2: when to stop
searching for a base case, when to stop changing the design, and when to stop searching for
better evaluation procedures. It is also relevant for modeling and theory building. To indicate the
different design goals in a satisficing exploration of alternatives, figure 2 links the artifacts of the
different types of design research activity to their primary design goal, the stop rule. The
functionality, relevance, novelty, and utility are pragmatic goals for different facets involved in
identifying and developing an operational design. In contrast, explanatory power and validity of
representations are important for deciding on when explanatory theories are adequate.
But how do we know exactly when it makes sense to stop the search for alternatives in,
say, attempts at moving the performance frontier? Is there a way to foresee the outcome of the
exploration activity while it is in progress and optimize the effort? The answer is that there is
not: because of unintended consequences and bounded rationality, it is impossible to outline the
performance frontier until the alternatives actually are explored. Also the representation of the
performance frontier is in flux until alternatives are known. Satisficing is the only alternative in
explorative activity, as Simon (1996) observed. Here, it is also important to note that only the
18
decision-maker or problem solver knows when to stop. An external observer cannot know what
the problem solver really wants and when he or she is satisfied (from a scientific point of view,
this is the Achilles’ heel of the satisficing concept). The only way to get at someone else’s
intention would be through a hermeneutic research approach. Since this paper builds on scientific
realism, we have to rule out the possibility of scientifically being able to get an empirical grasp
of someone else’s intentions.
The satisficing process of discovery is iterative because the representations that make the
task understandable are difficult to specify before the solutions have been found. This is perhaps
the most fundamental challenge for formally describing discovery. However, attempts have to be
made because without a description of problem solving and discovery it is not possible to learn
from experience and improve how design science research and exploration is conducted.
4. An Example of Design Science in OM
In the remainder of the paper, we discuss a case to illustrate how the solutions that affect the
performance frontier were discovered after new information technology became available. The
setting for the case is CERN, the European Organization for Nuclear Research (www.cern.ch),
and we explore how the Internet was adopted by the High-Energy Physics (HEP) community as
the tool to communicate and share information needed to design and construct the next-
generation particle accelerator. Experimental physics involves complex machines and
experiments to test and validate the predictions of the currently prevailing theory of the universe.
The technological complexity and the high costs involved in building these instruments are of
such a scale that the whole global HEP community is required to realize them. At CERN,
progress towards more efficient workflow management took place in multiple locations across
19
the HEP community. One of the authors of this paper spent five years as the responsible
researcher of the case software project at CERN.
The efficiency and flexibility of a major CERN project is constrained by how well
geographically distributed design, delivery, and construction operations can be co-ordinated and
managed. This is indeed the classical question of organizational integration (e.g., Lawrence and
Lorsch, 1967). Thus, improving information sharing and coordination is a means for moving the
performance frontier in High Energy Physics operations. When the World Wide Web (the Web
henceforth) was invented in 1992 at CERN by computer scientist Tim Berners-Lee (Berners-Lee,
1999; Hameri and Nordberg, 1998), the potential for improving the operational performance of
CERN increased considerably. However, actually improving the performance required finding
the problem situations and specific solutions where the new invention could improve the
operation.
The situation at CERN in early 1990s resembled the situation with the early pioneers of
networking technologies (Gillies and Cailliau, 2000). The rudimentary set-up to exchange
electronic messages between the islands of Hawaii in the beginning of 1970s generated the first
solutions that even today provide the foundation for networking. The ALOHANET and other
contemporary and independent trials provided solutions and designs for packet-switching and
time-sharing between various terminals using the same transmission resources. In a similar way,
the birth of the Web integrated various known technologies, mark-up languages, graphical user
interfaces and heterogeneous hardware, into one information- and resource-sharing whole.
Prior to the Web, the community of globally scattered physicists, equipment suppliers,
and contractors each managed their projects independently by using phone, fax, and e-mail. The
Web enabled geographically distributed projects to be integrated and coordinated with less
20
management and organizational set-up by developing and adopting Web-based systems to
manage administrative information, key parameters of the instruments and related documents.
In the following, the problem-solving research that introduced Web tools into HEP
operations at CERN is described using a structure, specifically, a structure based on the design
science approach presented in the previous section. The progress of the example research is
described and linked to the research artifacts presented in figure 3. The link is indicated by a
reference in the following format in the text: [Artifact, Instance]. The artifacts are
implementations, base cases or mid-range theories, solution designs, evaluations, representations,
and deep-structure explanations created in the research process. The instance is simply a counter
that links a description of how the research proceeded in the text to the figure 3. For instance,
“[Base case, #5]” refers to the base case developed at the fifth step of the development process.
Insert Figure 3 here
4.1 Problem discovery and solution design
The challenge faced at the inception of an explorative and problem-solving research endeavor is
to find relevant problem situations and novel solution designs. The base case [Base case, #1] in
the example was the use of the Web to display project information linked to the bill-of-material
(BOM) to provide access to part-item-related documents as well as to general project
documentation over the internet, which started in 1995. The goal was to reduce search time for
the users, have more fluent information flow and to reduce duplication of data and work in the
complex project network.
The system design that was developed to achieve the goal provided different users with
customized views to the information [Solution design, #2]. This meant that the physicist could
“see” a different structure than the electronics engineer. Being a globally distributed project,
21
access was given to suppliers and other contributors of the project. However, this access was
controlled such that information common to all participants was shared without limitations, but
access to the more delicate information, such as contract-related information, was limited.
Six months after the start of the research the first implementation trials were underway
[Implementation, #3]. Although the design was very simple in comparison to the state-of-the-art
engineering information systems at the time, the easy access through a Web browser
immediately made the system popular among various sub-projects throughout the HEP
community.
The first base case [Base case, #1] focused on using the Web for distributed but
structured document management based on the end-product BOM. However, an evaluation of the
solution design that was implemented suggested that adding more complex functionality could
bring further benefits [Evaluation, #4]. Specifically, using the Web to coordinate the engineering
change process (controlling changes in the documents of the project) would enhance the
efficiency of the design process. This led to a new base case, which incorporated the premise that
when change processes were managed over the internet, fewer face-to-face project reviews
would be required [Base case, #5].
The engineering change process is in many ways analogous to a production process, as it
involves a process with successive value-adding steps, just like in any production system
(Hameri and Nihtilä, 1998). This analogy is important, because it led to the first formal problem
representation [Representation, #6]. The analogy further steered the project toward a conscious
application of tested OM approaches to document workflow [Base case, #7]. The key
characteristic of the re-designed solution was that the process flow and status codes for different
document classifications could now be formally defined [Solution design, #8]. In a sense, the
22
document classifications were treated much like product families with their respective flows,
resource requirements and planning principles.
Once the workflows were supported by the system, documents could simply be advanced
by changing their statuses accordingly [Implementation, #9]. This required the development of
various new metrics of material-flow management [Solution design, #10]. The first such metrics
were the average lead times to handle change requests, along with their standard deviations
[Implementation, #11]. The introduction of these metrics immediately led to major
improvements by reducing lead times and improving punctuality, because management efforts
could now be focused on those documents in the workflow that had delays [Base case, #12].
However, because some groups had difficulties in relating these measures to the overall project
progress, the effect of these metrics was not identical across the HEP community [Evaluation,
#13]. This led to the integration of project schedules, for instance, by integrating aggregate
planning of a manufacturing unit with the detailed operations of the project [Base case, #14;
Solution design, #15].
Working with implementing document change management and adopting OM practices
led to the realization that an information logistics approach to distributed project operations
could be formulated [Mid-range theory, #16]. The mid-range theory specifies the use of general
flow metrics of lead times, punctuality and something called the brain inventory value, which
was calculated based on the hourly value of the work-in-process.1 These flow metrics were
complemented with specifications of how to manage overall communication flows. Novel
1 The “hourly value of the WIP” is the value of the work in progress of the document process. For example, if an
engineer has spent 10 hours on a design task that is waiting for approval, and the cost rate of his design work is
40€/h the hourly value of the WIP is 400€.
23
communication matrices that describe exactly who communicates and how2 were introduced to
the solution design together with some overall maps and cluster techniques to analyse the
communication patterns [Solution design, #17] (The Economist 2001).
Developers found that the problem leading to reduced efficiency was that work in
distributed engineering projects take place in bursts and across the traditional line organization
chart [Evaluation, #18]. To solve this problem, period batch control with fixed control cycles
was introduced for the scheduling of project tasks (Burbidge, 1989), which led to further
theoretical insight [Mid-range theory, #19]. Planning cycles and levelling the load facilitate the
use of predefined routes and workflows for handling individual change requests more efficiently
and quickly. This way the general technique of production-flow analysis (Burbidge, 1989) was
applied to the management of distributed document operations [Representation, #20].
At this point, in late 1998, the base case and the related design had evolved to the point
where the first commercial product was born [Solution design, #21]. The commercial system
developed—Kronodoc (www.kronodoc.com)—helps manage the time and progress around
document processes (Byckling et al., 2000). Initially developed for the use of the HEP
community, Kronodoc has since been implemented at major power station construction sites,
shipyards and in the delivery of major industrial installations [Implementation, #22].
4.2 Evaluation and theoretical analysis
In the Web case, it is the application of OM-related know-how of efficient material flow
management to the flows of documents in project business environment that provided the basis
2 For example, if three people in a project open and modify the same design document there is a communication link
through that document. By recording linking events of this type it is possible to describe the communication
network and workflow outside the formal project organization.
24
for the commercialized solution design (Hameri and Nihtilä, 1997; Hameri and Nihtilä, 1998;
Hameri and Nihtilä, 1998; Hameri, Nihtilä and Rehn, 1999). The concept of information logistics
is a mid-range theory [Mid-range theory, #19] that evolved from the base case and applies the
basic concepts of swift and even flow (Schmenner and Swink, 1998) and production flow theory
(Burbidge, 1989) to the distributed document processes that are needed to deliver complex end
products.
An evaluation procedure [Evaluation, #23] developed after introducing Kronodoc
commercialization enabled an assessment of the potential benefits that individual customers
could realize by implementing the new workflow management tools. This evaluation procedure
was based on the documented progress of numerous implementation cases both inside and
outside of the HEP community (Hameri and Nihtilä, 1998).
The evaluation of implementations revealed a number of contingencies in the application
of the new tools. Many implementers were content with simple document archiving, but a
number of other implementers with well-established managerial routines were better able to
exploit the opportunities, compared to those managing their projects more on an ad hoc basis.
Advanced customers achieved significant reductions in overall project lead times,
simultaneously improving on-time delivery. Engineering companies working with many
customers and projects in parallel were the first to understand how the tool offered them the
opportunity to manage their work-in-progress (that is, the brain inventory) better, and how this
can improve throughput by up to 20 percent without increasing capacity. However, it may take
several years for a large company to achieve significant improvements.
Despite demonstrated benefits from implementations and a solid mid-range theory
describing the problem situations and goals that can be achieved with the system, acquiring
25
customers that actually start to use the system has been a slow process. Even though the basic
message is clear—reduction of document lead times and improving punctuality—companies
have had difficulties in starting to use the tools. Kronodoc is currently developing a detailed step-
wise model of how to implement the tools in different types of organizations [Mid-range theory,
#24].
The problem and problem resolution in the example can be formally represented in terms
of document workflows and managing the workflow [Representation, #20]. The workflows are
managed through the use of status codes attached to the individual documents and differentiated
handling rules in specific nodes of the workflow. However, the problem situation can also be
represented as an information chain of actors (Kåhre, 2002) passing on the information that is
used to control and monitor operations. The Web-based workflow management changes the
information chain of the distributed engineering projects. Focusing on the transfer of
information, information can only be passed on or lost in the information chain. If a high
proportion of the control and management information is not passed on along with the workflow,
but distributed through a central node the information chain becomes inherently more efficient.
The difference is best illustrated by considering what happens when a handling rule needs to be
changed. This illustration corresponds to a new formal representation of the problem situation
and solution design [Representation, #25].
The first alternative for changing the handling rule represents a situation before a
workflow management system is introduced. In this alternative, the new rule is communicated
sequentially through the chain of handling nodes. In this alternative, there is a risk at each
interface that the instruction will not be passed on to the following node, and the number of
communication steps in the information chain is equal to the number of handling nodes.
26
Problems are typically resolved by negotiation and collaboration between nodes. To ensure a
change in handling has in effect been made it is necessary to back-track the whole chain of
handling nodes.
The second alternative is based on the implementation of workflow management. Here,
the new rule is distributed by a central integrating node, which in distributed engineering projects
is typically the project manager. The number of transmission steps is in this situation reduced
from the number of nodes in the workflow to only two. Step one is informing the integrator and
step two is the integrator informing all nodes.
However, the drawback with this option for workflow management is that the nodes need
to be informed by the integrator about rule change before a handling event. A more loosely
coupled approach that is possible in a Web environment is to distribute control information to
handling nodes by reference to document specific integrators, or document agents. This reduces
the number of transmission steps from two to one, and removes the requirement that nodes be
informed before a handling event.
The information chain analysis can in a fairly straightforward manner be linked to the
scale of increasingly complex co-ordination devices available to organizations. Galbraith (1972)
proposed a Guttman-type scale of continuously increasing complexity in the co-ordination
devices of organizations. The scale progresses from rules and programs, hierarchy, plans, direct
contact, liaison, task force, teams, integrators, integrating departments, to eventually matrix
organizations (Kolodny, 1979). Reducing the need for complex integrating mechanisms
potentially move the performance frontier in situations where there are many and changing
parties involved in the operation.
27
Workflow management and document-centric integration represent introducing new
rules, programs, or plans. The introduction directly affects the need for other integrating devices
such as direct contact, liaison, task force, teams, integrators, integrating departments, and
complex matrix organizations. Simplifying the information chain with a workflow management
tool makes it possible to rely on process integration or document centric control mechanisms
which free management and other resources for more value adding uses [Theory, #26].
4.3. Discussion
The Web example described above is an illustration of how a design science approach can be
used to explore and eventually to shift the performance frontier. While this evidence is anecdotal
and qualitative, we know that companies that have been using the system over several years have
indeed witnessed that design lead time of complex product deliveries have reduced and
throughput improved by as much as 20%. The example also illustrates how organizations could
develop an approach for enhancing performance using a design science approach, and how
academic researchers could play an active—not just evaluative—role in the process. This process
must further be viewed as a process of both development and application of new tools; extant
OM research has pretty much focused on evaluative studies of application.
The Web example further illustrates how the new solution designs that were developed
made it possible for some distributed project networks to reduce their reliance on the more
intensive (and expensive) integration mechanisms such as mutual adjustment and joint problem-
solving. The introduction of new technology led to the creation of simpler types of integrating
mechanisms that opened up opportunities for companies to operate faster, more customized and
more reliably with less management intervention and less organizational complexity. Figure 4
summarizes the key outcomes of the research example using the terms introduced in figure 2.
28
Fundamentally, our in-depth example illustrates how exploring the application of new
technologies in a trial-and-error process led to insights on how to change the information chain
in a network environment to enhance performance in multiple dimensions.
Insert Figure 4 here
The long development time in the example illustrates that an important impact of
emergent technology on OM phenomena is that there are more unintended consequences and
alternative ways to solve an operations management problem using emergent technology than
can be comprehensively explored and evaluated by any one individual, research group, or
company. Unintended consequences coupled with bounded rationality lead to iterative processes
that can span several years. This further highlight the challenges associated with attempts at
moving the performance frontier: performance improvements inside the frontier can be much
faster compared to the speed at which the performance frontier can be moved.
5. Conclusion
We have proposed a methodology and philosophical basis for conducting discovery and
problem-solving research in OM. Our approach is rooted in the pioneering work of Herbert
Simon and his colleagues (Klahr and Simon, 1999; Simon, 1973). The primary challenge in
explicating a logic of discovery lies in delineating the ways in which ill-structured problems can
be tackled rigorously. We have outlined an approach that proceeds through experimentation and
trial and error towards theoretical accounts and while solving the problem at hand. We propose
that a more systematic design science approach is a good candidate for providing a solid basis for
explorative OM research. Such an approach can then be complemented by the more familiar and
established evaluation, theory-development and theory-testing approaches, which are already
29
well known in the OM research community (Handfield and Melnyk, 1998; Mentzer and Kahn,
1995; Voss, Tsikriktsis and Frohlich, 2002).
Many relevant OM phenomena are linked to ill-structured problems, moving the
performance frontier by development of new technologies being perhaps the best example. We
submit that OM research should explicitly address the challenges associated with the dynamics
of the performance frontier, because these are issues that are ultimately of great managerial
interest. We firmly believe that the mere evaluation of existing solutions is not sufficient to
maintain or build managerial interest in academic OM research. Like we said in the beginning of
this paper, good ideas must start somewhere. For us, this “somewhere” is the formulation of a
base case, which leads to more refined solutions designs, implementations and evaluation, and
ultimately, theoretical insight. OM researchers should incorporate these early stages of
development and discovery to their research agenda. In this paper, we have provided a
methodological and philosophical foundation for such research.
It should be evident that the design science approach delineated in this paper is by no
means intended to replace evaluative theory-testing (or even theory-building) research. Instead,
echoing Klahr and Simon’s (1999) ideas, the aim of design science is to complement existing
research approaches; design science can be used to produce novel and interesting research topics
for further evaluation and theorizing, much like Eisenhardtian (1989) case research can produce
novel theories for theory-evaluation research. That OM research should focus only on evaluation
is bad research policy. That we should focus on relevant and interesting research that maintains
high scientific rigor is a much better alternative.
30
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Figure 1
Emergent and semantic conception of design science
Design ideas
Phenomena
Design
Theory
Phenomena
Design
Conjecture Intended and unintended
consequences
Theoretical understanding of
problem discovery and solving
a) b) c)
Phenomena
Theory
37
Figure 2
Operations management as problem solving and design research
38
Figure 3
Progress in the Web case
Representation
Evaluation procedure
Solution design
Base case/ Mid-range theory
Implementation
Artefact
Resistance& refutation
Instance
Problem solving
Deep-structuretheory
#24, #19, #16, #14, #12, #7, #5, #1
#22, #11, #9, #3
#21, #17, #15, #10, #8, #2
#23, #18, #13, #4
#25, #20, #6
#26
39
Figure 4
Key outcomes from exploration to theoretical analysis in the illustrative case example
Evaluation
Solution design
Mid-range theory
Implementation and
phenomenaCommercialized tool that is used by many
large customers.
Description of the needs of different types of project environments and the impact of
step-wise introduction of the tool
Tool for describing and managing the process flow and status codes for different
document classes
Application of swift and fluent flow concepts to information logistics in project
business
Representation
Deep structure analysis
Information chain representation structures the problem and describes the
solution design
Simplified information chains reduce the risk for information loss and make it
possible to introduce simpler integrating mechanisms
40